Analysis and detection of functional outliers in water quality parameters from different automated monitoring stations in the Nalon River Basin (Northern Spain)

被引:6
|
作者
Pineiro Di Blasi, J. I. [1 ]
Martinez Torres, J. [2 ]
Garcia Nieto, P. J. [3 ]
Alonso Fernandez, J. R. [4 ]
Diaz Muniz, C. [4 ]
Taboada, J. [1 ]
机构
[1] Univ Vigo, Dept Nat Resources & Environm Engn, Vigo 36310, Spain
[2] Acad Militar, Ctr Univ Def, Zaragoza 50090, Spain
[3] Univ Oviedo, Fac Sci, Dept Math, Oviedo 33007, Spain
[4] Spanish Minist Agr Food & Environm, Cantabrian Basin Author, Oviedo 33071, Spain
关键词
Functional data analysis; Outliers; Water quality monitoring; Water Framework Directive; Water pollution; Functional depth; NOX LEVELS; ESTUARY; ECOSYSTEMS; BOOTSTRAP; RECOVERY;
D O I
10.1007/s11356-014-3318-5
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The purposes and intent of the authorities in establishing water quality standards are to provide enhancement of water quality and prevention of pollution to protect the public health or welfare in accordance with the public interest for drinking water supplies, conservation of fish, wildlife and other beneficial aquatic life, and agricultural, industrial, recreational, and other reasonable and necessary uses as well as to maintain and improve the biological integrity of the waters. In this way, water quality controls involve a large number of variables and observations, often subject to some outliers. An outlier is an observation that is numerically distant from the rest of the data or that appears to deviate markedly from other members of the sample in which it occurs. An interesting analysis is to find those observations that produce measurements that are different from the pattern established in the sample. Therefore, identification of atypical observations is an important concern in water quality monitoring and a difficult task because of the multivariate nature of water quality data. Our study provides a new method for detecting outliers in water quality monitoring parameters, using turbidity, conductivity and ammonium ion as indicator variables. Until now, methods were based on considering the different parameters as a vector whose components were their concentration values. This innovative approach lies in considering water quality monitoring over time as continuous curves instead of discrete points, that is to say, the dataset of the problem are considered as a time-dependent function and not as a set of discrete values in different time instants. This new methodology, which is based on the concept of functional depth, was applied to the detection of outliers in water quality monitoring samples in the Nalon river basin with success. Results of this study were discussed here in terms of origin, causes, etc. Finally, the conclusions as well as advantages of the functional method are exposed.
引用
收藏
页码:387 / 396
页数:10
相关论文
共 50 条
  • [11] Water quality for different uses in the main groundwater bodies of the Guadalquivir River Watershed, Atlantic Basin, Spain
    Perea, Rocio
    Rodriguez-Rodriguez, Miguel
    ENVIRONMENTAL EARTH SCIENCES, 2009, 59 (01) : 75 - 86
  • [12] Water quality for different uses in the main groundwater bodies of the Guadalquivir River Watershed, Atlantic Basin, Spain
    Rocio Perea
    Miguel Rodríguez-Rodríguez
    Environmental Earth Sciences, 2009, 59 : 75 - 86
  • [13] Assessment of water quality parameters using multivariate analysis for Klang River basin, Malaysia
    Ibrahim Mohamed
    Faridah Othman
    Adriana I. N. Ibrahim
    M. E. Alaa-Eldin
    Rossita M. Yunus
    Environmental Monitoring and Assessment, 2015, 187
  • [14] Assessment of water quality parameters using multivariate analysis for Klang River basin, Malaysia
    Mohamed, Ibrahim
    Othman, Faridah
    Ibrahim, Adriana I. N.
    Alaa-Eldin, M. E.
    Yunus, Rossita M.
    ENVIRONMENTAL MONITORING AND ASSESSMENT, 2015, 187 (01) : 1 - 12
  • [15] Monitoring water quality of the Sergipe River basin: an evaluation using multivariate data analysis
    Hora Alves, Jose do Patrocinio
    Fonseca, Lucas Cruz
    Alves Chielle, Raisa de Siqueira
    Barreto Macedo, Lucia Calumby
    RBRH-REVISTA BRASILEIRA DE RECURSOS HIDRICOS, 2018, 23
  • [16] Water quality big data analysis of the river basin with artificial intelligence ADV monitoring
    Chen, Z. Y.
    Meng, Yahui
    Wang, Ruei-yuan
    Chen, Timothy
    MEMBRANE AND WATER TREATMENT, 2022, 13 (05): : 219 - 225
  • [17] COMPARATIVE ANALYSIS OF THE WATER QUALITY AT MURES RIVER BASIN BASED ON MEASURED PARAMETERS AND WATER POLLUTION INDEX
    Blidar, Elena-Violeta
    Gavrilas, Simona
    Ursachi, Claudiu-Stefan
    Perta-Crisan, Simona
    Munteanu, Florentina-Daniela
    SCIENTIFIC STUDY AND RESEARCH-CHEMISTRY AND CHEMICAL ENGINEERING BIOTECHNOLOGY FOOD INDUSTRY, 2024, 25 (04): : 377 - 392
  • [18] Optimal Location of Water Quality Monitoring Stations Using an Artificial Neural Network Modeling in the Qarah-Chay River Basin, Iran
    Goudarzi, Fatemeh
    Hedayatiaghmashhadi, Amir
    Kazemi, Azadeh
    Fuerst, Christine
    WATER, 2022, 14 (06)
  • [19] Statistical analysis of water quality parameters in the basin of the Nisava River (Serbia) in the period 2009-2018
    Stricevic, Ljiljana
    Pavlovic, Mila
    Filipovic, Ivan
    Radivojevic, Aleksandar
    Bursac, Natasa Martic
    Gocic, Milena
    GEOGRAFIE, 2021, 126 (01): : 55 - 73
  • [20] Monitoring Water Quality Parameters Using Sentinel-2 Data: A Case Study in the Weihe River Basin (China)
    Liu, Tieming
    Guo, Zhao
    Li, Xiaoping
    Xiao, Teng
    Liu, Jiaxin
    Zhang, Yuanzhi
    SUSTAINABILITY, 2024, 16 (16)